Seizure detection methods, apparatus, and systems using a wavelet transform maximum modulus algorithm

ABSTRACT

Methods for detecting a seizure, by use of a wavelet transform maximum modulus (WTMM) algorithm applied to body data. A non-transitive, computer-readable storage device for storing data that when executed by a processor, perform such a method.

The present application claims priority from U.S. provisional patentapplication Ser. No. 61/547,567, filed on Oct. 14, 2011, which isincorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to the field of epileptic eventdetection. More particularly, it concerns epileptic event detection byuse of a wavelet transform maximum modulus (WTMM) algorithm on a timeseries of patient body signal data.

2. Description of Related Art

There have been various advancements in the area of seizure detection,which remains a fairly subjective endeavor. The task of automateddetection of epileptic seizures is intimately related to and dependenton the definition of what is a seizure, definition which to date issubjective and thus inconsistent within and among experts. The lack ofan objective and universal definition not only complicates the task ofvalidation and comparison of detection algorithms, but possibly moreimportantly, the characterization of the spatio-temporal behavior ofseizures and of other dynamical features required to formulate acomprehensive epilepsy theory.

The current state of automated seizure detection is, by extension, afaithful reflection of the power and limitations of visual analysis,upon which it rests. The subjectivity intrinsic to expert visualanalysis of seizures and its incompleteness (it cannot quantify orestimate certain signal features, such as power spectrum) confound theobjectivity and reproducibility of results of signal processing toolsused for their automated detection. What is more, several of thefactors, that enter into the determination of whether or not certaingrapho-elements should be classified as a seizure, are non-explicit(“gestalt-based”) and thus difficult to articulate, formalize andprogram into algorithms.

Most, if not all, existing seizure detection algorithms are structuredto operate as expert electroencephalographers. Thus, seizure detectionalgorithms that apply expert-based rules are at once useful anddeficient; useful as they are based on a certain fund of irreplaceableclinical knowledge and deficient as human analysis biases propagate intotheir architecture. These cognitive biases which pervade human decisionprocesses and which have been the subject of formal inquiry are rootedin common practice behaviors such as: a) The tendency to rely tooheavily on one feature when making decisions (e.g., if onset is notsudden, it is unlikely to be a seizure because these are paroxysmalevents); b) To declare objects as equal if they have the same externalproperties (e.g., this is a seizure because it is just as rhythmical asthose we score as seizures) or c) Classify phenomena by relying on theease with which associations come to mind (e.g., this pattern looks justlike the seizures we reviewed yesterday).

Seizure detection algorithms' discrepant results make attainment of aunitary or universal seizure definition ostensibly difficult; the notionthat expert cognitive biases are the main if not only obstacle on thepath to “objectivity” is rendered tenuous by these results. Thesedivergences in objective and reproducible results may be attributable inpart, but not solely, to the distinctiveness in the architecture andparameters of each algorithm. The fractal or multi-fractal structures ofseizures accounts at least in part for the differences in results anddraws attention to the so-called “Richardson effect.” Richardsondemonstrated that the length of borders between countries (a naturalfractal) is a function of the size of the measurement tool, increasingwithout limit as the tool's size is reduced. Mandelbrot, in his seminalcontribution “How long is the coast of Britain,” stressed thecomplexities inherent to the Richardson effect, due to the dependency ofparticular measurements on the scale of the tool used to perform them.Although defining seizures as a function of a detection tool would beacceptable, this approach may be impracticable when comparisons between,for example, clinical trials or algorithms are warranted. Anotherstrategy to bring unification of definitions is to universally adopt theuse of one method, but this would be to the detriment of knowledgemining from seizure-time series and by extension to clinicalepileptology.

To date, meaningful performance comparisons among myriad existingalgorithms have not been feasible due to lack of a common and adequatedatabase. However, even if adequate databases were available, the valueof such “comparisons” would be limited by the absence of a universallyaccepted definition of what is a “seizure.” The previously notedcognitive biases and architectural/parametric distinctions amongalgorithms impede achievement of consensus and in certain cases even ofmajority agreement in classifying particular events as seizures ornon-seizures. Because expert visual analysis provides the benchmarks(seizure onset and termination times) from which key metrics (detectionlatency in reference to electrographic and clinical onset time (“speedof detection”), sensitivity, specificity and positive predictive value)are derived, the effects of cognitive biases propagate beyond theseizure/non-seizure question into other aspects of the effectiveness ofa particular seizure detection algorithm.

SUMMARY OF THE INVENTION

In one aspect, the present disclosure relates to a method, comprisingreceiving a time series of a first body signal of the patient;determining at least two sliding adjacent time windows for said timeseries of said first body signal comprising a foreground window and abackground window; extracting at least one first long chain from saidforeground window and at least one second long chain from saidbackground window; applying a continuous wavelet transform to each ofsaid long chains, to yield a wavelet transform maximum modulus (WTMM)skeleton for each window; determining a first variance of the WTMMskeleton from said foreground window and a second variance of the WTMMskeleton from said background window; generating a stepwiseapproximation of changes in said variance; determining a seizure onsetin response to a determination that an output of the stepwiseapproximation of changes in said first variance has reached an onsetthreshold; and determining a seizure termination in response to adetermination that an output of the stepwise approximation of changes insaid second variance has reached a termination threshold.

In one aspect, the present disclosure relates to a non-transitorycomputer readable program storage unit encoded with instructions that,when executed by a computer, perform a method as described above orherein.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention may be understood by reference to the followingdescription taken in conjunction with the accompanying drawings, inwhich like reference numerals identify like elements, and in which:

FIG. 1 illustrates a medical device system for detecting and classifyingseizure events related to epilepsy from sensed body data processed toextract features indicative of aspects of the patient's epilepsycondition;

FIG. 2 illustrates a medical device system, according to an illustrativeembodiment of the present disclosure;

FIG. 3 provides a stylized diagram of a medical device and differentdata acquisition units that may provide output(s) used by other unit(s)of the medical device, in accordance with one illustrative embodiment ofthe present disclosure;

FIG. 4 provides a stylized diagram of a medical device and differentdata acquisition units that may provide output(s) used by other unit(s)of the medical device, in accordance with one illustrative embodiment ofthe present disclosure;

FIG. 5 provides a stylized diagram of a seizure onset/termination unit,in accordance with one illustrative embodiment of the presentdisclosure;

FIG. 6 presents WTMM chains constructed from a signal, in accordancewith one illustrative embodiment of the present disclosure;

FIG. 7 presents a logarithmic standard deviation of an ECoG time seriesand a WTMM-stepwise approximation thereof, in accordance with oneillustrative embodiment of the present disclosure;

FIG. 8 illustrates the output of the WTMM algorithm in reference to anAverage Indicator Function (AIF) making use of four algorithms,including the WTMM algorithm, in accordance with one illustrativeembodiment of the present disclosure;

FIG. 9 shows a graph of a specificity function for the WTMM method as afunction of time with respect to a validated algorithm's time of seizuredetection, in accordance with one illustrative embodiment of the presentinvention;

FIG. 10 shows a plot of the decimal logarithm of the dependence ofseizure energy on seizure duration, in accordance with one illustrativeembodiment of the present disclosure;

FIG. 11 shows the empirical “tail” of the conditional probabilitydistribution functions for: (a) Seizure durations (minimum duration: 2sec); and (b) the logarithm of seizure energy as estimated with fourdifferent methods, in accordance with one illustrative embodiment of thepresent disclosure;

FIG. 12 provides a flowchart depiction of a method, in accordance withone illustrative embodiment of the present disclosure;

FIG. 13 shows ECoG before (upper panel) and after differentiation (lowerpanel), in accordance with one illustrative embodiment of the presentdisclosure; and

FIG. 14 shows temporal evolution of the decimal logarithm of the powerspectrum of differentiated ECoG.

DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

In one aspect, the present disclosure provides several new seizuredetection algorithms that may be applied to one or more streams of bodydata. Some of these algorithms rely principally on power variance fordetection of seizures, while others rely mainly on power spectral shape.

In another aspect, the present disclosure exploits the simultaneousapplication of two or more individual seizure detection algorithms toderive a probabilistic measure of seizure activity (PMSA), which may beused to issue detections by majority or consensus of a plurality of thetwo or more seizure detection algorithms, depending on safety factorsand others such as detection speed, sensitivity, specificity or anyother performance measures and the clinical application(s) at hand.Real-time (“on the run”) automated seizure detection provides the onlymeans through which contingent warning to minimize risk of injury topatients, delivery of a therapy for control of seizures, or logging ofthe date, time of onset and termination and severity may be performed.

This disclosure, in one embodiment, provides a wavelet transform maximummodulus (WTMM) algorithm suitable for use in epileptic event detection,by operation on a time series of patient body signal data. Such a WTMMalgorithm may be used by itself, as part of a Probabilistic Measure ofSeizure Activity (PMSA), or, by performing stepwise approximations ofthe WTMM, as a single algorithm capable of providing a PMSA.

More generally, this disclosure: a) draws attention to the intricaciesinherent to the pursuit of a universal seizure definition even whenpowerful, well understood signal analysis methods are utilized to thisend; b) identifies this aim as a multi-objective optimization problemand discusses the advantages and disadvantages of adopting or rejectinga unitary seizure definition; c) introduces a Probabilistic Measure ofSeizure Activity to manage this thorny issue.

Seizure detection belongs to a class of optimization problems known as“multi-objective” due to the competing nature between objectives:improvements in specificity of detection invariably degrade sensitivity,and vice-versa. Attempts to achieve a universal seizure definition usingobjective, quantitative means, are likely to be fraught with similarcompeting objectives. In one aspect, the present invention involves theapplication of tools from the field of multi-objective optimization,among others to make the problems caused by competing objectives moretractable.

Achieving a unitary seizure definition would be difficult, as consensusamong epileptologists as to what grapho-elements are classifiable asictal, is rare. In the absence of a universal definition, issuingseizure warnings for certain cases will be problematic and unsafe. Forexample, if a patient with seizures wishes to operate power equipment ora motor vehicle, the absence of a universal agreement on when thepatient has had a seizure may preclude any viable way of ensuring, usingseizure detection algorithms that the patient's seizures are undersufficient control to allow such activities to occur. To manage thedifficulties of a consensus seizure definition, substantive gains arefeasible through steps entailing, for example, the application ofadvanced signal analysis tools to ECoG, to hasten the identification ofproperties/features that would lead to the probabilistic discriminationof seizures from non-seizures with worthwhile sensitivity andspecificity for the task at hand. However, to even have a modicum ofsuccess, such an approach should not ignore the non-stationarity ofseizures and, should strike some sort of balance between supervised(human) and unsupervised machine-learning) approaches. The resultingmultidimensional parameter space, expected to be broad and intricate,may also foster discovery of hypothesized (e.g. pre-ictal) brainsub-states.

The challenges posed by the attempt to define seizures unitarily usingobjective means (distinct from visual analysis) may be partly related totheir fractal properties and understood through a simplistic analogy tothe so-called “Richardson effect”. A revision of the time-honoredsubjective definition of seizures may be warranted to further advanceepileptology.

The present inventors propose a Probabilistic Measure of SeizureActivity (PMSA) as one possible strategy for characterization of themulti-fractal, non-stationary structure of seizures, in an attempt toeschew the more substantive limitations intrinsic to other alternatives.

The PMSA may make use of “indicator functions” (IFs) denoted χ_(algo)for each algorithm ‘algo.’ In one embodiment, the PMSA may also make useof an Average Indicator Function (AIF). In one embodiment, the AIF isdefined as:

AIF(t)=(χ_(Val)(t)+χ_(r) ₂ (t)+χ_(STA/LTA)(t)+χ_(WTMM)(t))/4

The subscripts Val, r², STA/LTA and WTMM refer to four differentalgorithms, particular embodiments of which are described herein and/orin other related applications. One or more of these algorithms may beused to detected seizures from one or more body data streams including,but not limited to, a brain activity (e.g., EEG) data stream, a cardiac(e.g., a heart beat) data stream, and a kinetic (e.g., body movement asmeasured by an accelerometer) data stream.

“Val” refers to an algorithm for seizure detection using ECoG data thathas been validated by experts without reaching a universal consensusabout its performance (e.g., false positive, false negative and truepositive detections). An “r²” algorithm may also be referred to hereinas an “r̂²,” “autoregression,” or “autoregressive” algorithm. A “STA/LTA”algorithm refers to an algorithm characterized by the ratio of aShort-Term Average to a Long-Term Average. A “WTMM” algorithm refers toa Wavelet Transform Maximum Modulus algorithm.

For determination of an AIF from the foregoing formula, an algorithm'sIF equals 1 for time intervals (0.5 sec in this application) “populated”by ictal activity and 0 by inter-ictal activity. The IF's are used togenerate four stepwise time functions, one for each of: a) a 2^(nd)order auto-regressive model (r²); b) the Wavelet Transform MaximumModulus (WTMM); c) the ratio of short-to-long term averages (STA/LTA)and d) a Validated algorithm (Val). With these IFs, the AIF is computed(its values may range between [0-1] with intermediate values of 0.25,0.5 and 0.75 in this embodiment). (Intermediate AIF values are functionsof the number of algorithms applied to the signal. Since in this study 4methods were used and the range of the indicator function is [0-1], theintermediated values are [0.25, 0.5, 0.75]). These values [0-1] areestimates of the probability of seizure occurrence at any given time. Inanother embodiment, the values of each algorithm's IF may be weighteddifferently, and a composite IF (e.g., a Weighted Indicator Function orWIF) different from the AIF may be computed.

Data obtained from one subject undergoing evaluation for epilepsysurgery with intra-cranial electrodes was selected for analyses. TheECoG was recorded using electrodes implanted into the amygdala, peshippocampus and body of hippocampus bilaterally through the temporalneocortex and had a duration of 6.9 days (142′923′853 samples; 239.75 Hzsampling rate).

For efficient analyses, ECoG signal differentiation was performed, so asto minimize the non-stationarity present in them. If Z(t) is raw ECoG,then its difference is X(t)=Z(t)−Z(t−1), where (t) corresponds to asample time increment. This linear operation is exactly invertible and,unlike band-pass filtering or detrending, does not suppress lowfrequency fluctuations, but decreases their overall influence. FIG. 13illustrates the effect of this operation on raw ECoG. The differentiatedECoG is less non-stationary (chiefly at low frequencies) than theundifferentiated one (x-axis: time in sec.; y-axis: amplitude inmicrovolts). FIG. 14 shows a time-frequency map of the evolution of thepower spectra of differentiated ECoG segments (as shown in FIG. 13,bottom panel). The power spectra are estimated within 5 sec movingwindows of length. Six brief seizures appear as marked power spectrumincreases (red and specks of white) in the 10-100 Hz. band (x-axis: timein sec.; y-axis: frequency (Hz); color scale to the right of maingraph).

Continuous Wavelet Transform Maximum Modulus (WTMM)

The WTMM technique exploits the large, sudden changes in the variance ofsignal features, such as amplitude and frequency that characterize mostepileptic seizures. These features are calculated within a hierarchy ofsequences of short contiguous windows of equal length and are extractedin the form of chains of continuous Wavelet Transform Maximum Modulus(WTMM), using a mother wavelet defined as the 1^(st) order derivative ofthe Gaussian function ˜exp(−t²). The construction of long chains to forma WTMM-skeleton is a commonly used method for detecting changes in themean value of noisy signals such as edge detection in vision computerprograms. WTMM relies on three parameters: a) The length of the windowused to calculate the sequence of variance values; b) A parameter a*that defines which WTMM-chains are long and, c) The threshold ΔU for thestepwise approximation of the logarithmic variance curve for detectingthe onsets and ends of seizures.

The Wavelet Transform (WT) may be conceptualized as a “mathematicalmicroscope” that is well suited to reveal the hierarchy that governs thespatial distribution of singularities of multifractal measures. The roleof the scaling parameter a* is similar to that of the magnificationsetting in a microscope: the larger the value of a*, the larger thescale of the signal's structure under investigation. By using waveletsinstead of boxes, the smoothing effect of polynomials that might eithermask singularities or perturb the estimation of their strength (Holderexponent) is avoided. This approach remedies one of the main failures ofthe classical multifractal methods such as the box-counting algorithmsin the case of measures and of the structure function method in the caseof functions. Another advantage of this method is that the skeletondefined by the WTMM provides an adaptive space-scale partitioning fromwhich to extract the singularity spectrum via the Legendre transform ofthe scaling exponents (real, positive as well as negative) of somepartition functions defined from the WT skeleton. The person of ordinaryskill in the art is aware of mathematical description and applications,as well as theoretical treatment, of random multifractal functions.

As originally pointed out for the specific purpose of analyzing theregularity of a function, the redundancy of the WT may be eliminated byfocusing on the WT skeleton defined by its modulus maxima only. Thesemaxima are defined, at each scale a, as the local maxima of the set ofwavelet transforms over all possible position x at the fixed scale a ofthe function f denoted |T_(ψ)[f](x,a)|. These WTMM points are disposedon connected curves in the space-scale (or time-scale) half-plane,called maxima lines. Let us define L(a₀) as the set of all the maximalines that exist at the scale a₀ and which contain maxima at any scalea<a₀. An important feature of these maxima lines, when analyzingsingular functions, is that there is at least one maxima line pointingtowards each singularity.

There are almost as many analyzing wavelets as applications of thecontinuous WT. A commonly used class of analyzing wavelets is defined bythe successive derivatives of the Gaussian function:

$\begin{matrix}{{g^{(N)}(x)} = {\frac{d^{N}}{{dx}^{N}}{\exp \left( {{- x^{2}}/2} \right)}}} & (7)\end{matrix}$

Note that the WT of a signal f(x) with g(N) (Eq. 7) takes the followingsimple expression:

$\begin{matrix}{{{T_{g^{(N)}}\lbrack f\rbrack}\left( {x,a} \right)} = {{\frac{1}{a}{\int_{- \infty}^{+ \infty}{{f(y)}{g^{(N)}\left( {\left( {y - x} \right)/a} \right)}\ {y}}}} = {a^{N}\frac{d^{N}}{{dx}^{N}}{T_{g^{(0)}}\lbrack f\rbrack}\left( {x,a} \right)}}} & (8)\end{matrix}$

Equation (8) shows that the WT computed with g^((N)) at scale a isnothing but the N-th derivative of the signal f(x) smoothed by a dilatedversion g⁽⁰⁾(x/a) of the Gaussian function. This property is at theheart of various applications of the WT “microscope” as a very efficientmulti-scale singularity tracking technique.

The algorithm described as follows may be used to construct theWTMM-skeleton.

Let Y(t) be an arbitrary signal and consider the corresponding smoothedsignal obtained by using a scale-dependent kernel:

$\begin{matrix}{{c_{0}\left( {t,a} \right)} = {\int_{- \infty}^{+ \infty}{{{Y\left( {t + {av}} \right)} \cdot {\psi_{0}(v)}}\ {{v}/{\int_{- \infty}^{+ \infty}{{\psi_{0}(v)}\ {v}}}}}}} & (9)\end{matrix}$

where a>0 is a time scale and ψ₀(t) is some function decayingsufficiently fast on both sides of its single maximum; further on,ψ₀(t)=exp(−t²) shall be used. The wavelet function is defined as:

$\begin{matrix}{{\psi_{n}(t)} = {{\left( {- 1} \right)^{n} \cdot \frac{d^{n}{\psi_{0}(t)}}{{dt}^{n}}} \equiv {\left( {- 1} \right)^{n} \cdot {\psi_{0}^{(n)}(t)}}}} & (10)\end{matrix}$

Using integration by part and exploiting the fast decay of the functionψ₀(t) at t

±∞, the following formula for the Taylor's coefficients (the n-thderivative of the smoothed signal, divided by n!) of the smoothed signalis obtained:

$\begin{matrix}{{{c_{n}\left( {t,a} \right)} \equiv {\frac{1}{n!}\frac{d^{n}{c_{0}\left( {t,a} \right)}}{{dt}^{n}}}} = {\int_{- \infty}^{+ \infty}{{Y\left( {t + {av}} \right)}{\psi_{n}(v)}\ {{v}/a^{n}}{\int_{- \infty}^{+ \infty}{v^{n}{\psi_{n}(v)}\ {v}}}}}} & (11)\end{matrix}$

Equation (9) is a particular case of formula (II) for n=0.

The WTMM-point (t,a) for n≧1 is defined as the point for which|c_(n)(t,a)| has a local maximum with respect to time t for a given timescale a. For n=0, the WTMM-points are defined as points of localextremes (maxima or minima) of the smoothed signal c₀(t,a) that may bejoined to form chains; the set of all chains creates a WTMM-skeleton ofthe signal. If ψ₀(t) is a Gaussian function, then a given WTMM-skeletonchain does not end when the scale a is decreased. The WTMM-points forthe 1^(st) order derivative c₁(t,a) indicate time points of the maximumtrend (positive or negative) of the smoothed signal c₀(t,a) for thegiven scale. This allows temporal localization of “points” of large andabrupt changes of the mean value of a noisy signal such as ECoG; these“points” mark the times when rather long WTMM-chains begin to grow.

A stepwise approximation S_(Y)(t|a*) for the signal Y(t), is defined asa function which is equal to the sequence of constant values g_(k) inthe successive intervals tε[τ_(k)(a*),τ_(k+1)(a*)]. Here τ_(k)(a*) arethe beginnings of the WTMM-chains for c₁(t,a) which exceed the thresholdtime scale a* and

$\begin{matrix}{g_{k} = {\sum\limits_{t = {\tau_{k}{(a_{*})}}}^{\tau_{k + 1}{(a_{*})}}\; {{Y(t)}/\left( {{\tau_{k + 1}\left( a_{*} \right)} - {\tau_{k}\left( a_{*} \right)} + 1} \right)}}} & (12)\end{matrix}$

is equal to the mean value of Y(t) within the time interval[τ_(k)(a*),τ_(k+1)(a*)].

For example, consider a signal that is a sum Y(t)=S₀(t)+ε(t), t=1, . . ., 2000, where ε(t) is a Gaussian white noise with unit standarddeviation and

S₀(t)=0 for tε[1,250], [751,1250], [1751,2000];

S₀(t)=2 for tε[251,750], [1251,1750].

The resulting stepwise approximation for this signal using WTMM-chainsis shown in FIG. 6, (noting that for both panels, the x-axis representstime (in sec)). (a) y-axis: Wavelet Transform Maximum Modulus-chainsconstructed using the 1st derivative of the Gaussian wavelet for thesignal S₀(t)+ε(t) defined by expression (12) (grey lines in FIG. 3 b).The solid lines correspond to WTMM-chains for negative c₁ (t,a) valuesand the dashed lines to WTMM-chains for positive c₁(t,a) values. Thethin grey horizontal line indicates the time scale threshold a*=50; (b)y-axis: The grey lines represent the signal S₀(t)+ε(t) (equation (12)),the solid horizontal line is the pure signal S₀(t) without noise and thedashed horizontal line the reconstructed signal S_(Y)(t|a*) using theWTMM method for a*=50.

Thus, times of abrupt changes of the mean value of the signal ofinterest can be estimated as instants of rather large steps of thesignal S_(Y)(t|a*) with an appropriate choice of the time scalethreshold a*.

Seizure Detection with the WTMM-Method

The WTMM-method of constructing a stepwise approximation S_(Y)(t|a*) foran arbitrary signal Y(t) may be applied to the task of seizuredetection. This is suggested from observations of seizures as intervalsof large, abrupt signal variance. Let us calculate sample estimates ofthe variance of the normalized ECoG X(t) within “small” adjacent timeintervals of length L and take the logarithmic values of the standarddeviations:

$\begin{matrix}{{{U\left( \xi_{j} \right)} = {\frac{1}{2} \cdot {\lg \left( {V_{X}\left( \xi_{j} \right)} \right)}}},{{V_{X}\left( \xi_{j} \right)} = {\frac{1}{L}{\sum\limits_{t = {\xi_{j} - L + 1}}^{\xi_{j}}\; {X^{2}(t)}}}},{\xi_{j} = {j \cdot L}},{j = 1},2,\ldots} & (13)\end{matrix}$

Thus, U(ξ_(j)) is a time series with a sampling time interval which is Ltimes larger than the sampling time interval of the original ECoG.Graphics of U are plotted as a function of the time position of theright end of these time intervals of length L and its stepwiseapproximation S_(U)(ξ_(j)|a*) is constructed, where a* is a scalethreshold for the decimal logarithms of the variance U (FIG. 7) of theECoG. Specifically, in FIG. 7, the grey line indicates values of thelogarithmic standard deviation of the time series of ECoG in a rat model(Rat48 increments), estimated within adjacent time intervals of lengthL=240. The thick black line is the WTMM-stepwise approximation ofLg(St.Dev.) with time scale threshold a*=3.

Seizures onsets and terminations are defined by the following rule:

ξ_(j) is onset if S _(u)(ξ_(j+1) |a*)−S _(U)(ξ_(j) |a*)≧ΔU

τ_(j) is end if S _(U)(ξ_(j+1) |a*)−S _(U)(ξ_(j) |a*)≦ΔU  (14)

The time interval length L, the scale threshold a* for the decimallogarithms of the variance and the threshold value ΔU for the logarithmof the variance of the normalized ECoG are this method's threeparameters. In the application shown in FIG. 7, the chosen parametervalues are the following: (L=240, a*=3, ΔU=0.0.25). The value of thethreshold ΔU determines the “strength” required to detect ECoG activitythat qualifies as seizures. Given a window L=240 for calculating thesequence of U(ξ_(j)) and a sampling rate of 240 Hz, the time resolutionof the WTMM-seizures detection implementation shown in FIG. 7 is 2 s.

The WTMM and Validated algorithms have been described in U.S.provisional patent application Ser. No. 61/547,567, filed on Oct. 14,2011, which is hereby incorporated by reference herein in its entirety.

The total number of detections, their duration and the percent timespent in seizure over the time series total duration (6.9 days) arepresented in Table 1.

TABLE 1 Summary statistics obtained by applying two different detectionmethods (Validated Algorithm; WTMM). The minimum duration of seizureswas set at 2 s. Validated algorithm WTMM Total number of seizures 318410795 with duration ≧ 2 s. Mean duration, s. 3.8 18.6 Median duration,s. 3.4 6 % time spent in seizure 2 34

FIG. 8 illustrates the output of the WTMM algorithm in comparison withan AIF making use of each of the validated algorithm, and the r2 (AR),STA/LTA and WTMM algorithms. Specifically, FIG. 8 shows results ofapplying the WTMM seizure detection method to a differentiated ECoG (inblack; 200 sec/panel) of a human with pharmaco-resistant epilepsy. Thegrey boxes represent the values (right y-axis) of an Average IndicatorFunction in the interval [0,1]. Seizures onset and end times determinedby the WTMM algorithm are indicated by vertical lines, with onset linesshifted upward and end lines, downward. Notice that the value of theAverage Indicator Function is rarely 1, at onset or termination,indicating all methods do not detect the ECoG activity as being ictal innature at those moments. However, with seizures exceeding certainduration (at least 20 seconds) and intensity thresholds, they convergeto all detect the seizure event. This indicates that the spectral andother properties of seizures are not homogeneous at the onset andtermination of seizures, which is consistent with the lack of agreementamong human experts (and algorithms) during onset and termination. Lefty-axis: ECoG amplitude (in μV); excursions above zero correspond topositive, and below, to negative, polarity.

Table 2 provides further evidence that, at some point in time, themajority of seizures detected by the validated algorithm are alsodetected by the WTMM method, with WTMM detecting 97% of seizures. Morespecifically and by way of example, the value 0.971 in Table 2 meansthat the WTMM method detections encompass 97.1% of seizure timeintervals detected with the validated method, with the exception of 1.6s. that correspond to the delay/lag between them in detecting seizureonsets (see below for details).

Time intervals for which the pairwise product χ_(Val)(t)·χ_(WTMM)(t)=1correspond to seizures detected by both the validated algorithm andWTMM. Dividing the number of time intervals whenχ_(Val)(t)·χ_(WTMM)(t)=1 by the number of intervals when χ_(Val)(t)=1,yields the specificity of the WTMM method with respect to the validatedalgorithm. The specificity functions for the two other methods Spe_(r2)_(—) _(Val)(τ) and Spe_(STA/LTA) _(—) _(Val)(τ) are identically computedand their maximum value (dependent on τ) may be regarded as the meanvalue of the time delay of one method's function with respect to anotherfor seizure onset and end times. Since the validated algorithm has aninherent delay of 1 s (the median filter's foreground window is 2 s) andan additional duration constraint of 0.84 s. is imposed before adetection is issued, its onset and end times are “delayed” compared tothose yielded by the WTMM method.

The present inventors discovered that the time differences are negativefor all three algorithms compared to the validated algorithm; that is,the validated algorithm's detection times lag behind those given by theother methods. More particularly, the mean delay of the validatedalgorithm is 1.6 s with respect to WTMM. The re-calculated specificityvalues shifted by τ shown in Table 2 are higher compared to thosewithout shifting.

TABLE 2 Values of specificity of WTMM calculated with respect to thevalidated method and time lag (as defined in the text) at which thespecificity attains its largest value. Method SPe_(Method)_ Val (0)$\max\limits_{\tau}{{Spe}_{Method\_ Val}\mspace{11mu} (\tau)}$$\underset{\tau}{\arg \; \max}\mspace{11mu} {Spe}_{Method\_ Val}\mspace{11mu} (\tau)$WTMM 0.823 0.971 −1.6 s

The information in Table 2 is also depicted graphically in FIG. 9, whichillustrates a graph of a specificity function for the WTMM method as afunction of time with respect to the Validated algorithm's time ofseizure detection. Tau (τ) zero (x-axis) corresponds to the time atwhich Val issues a detection. Negative τ values indicate “late”detections by the validated algorithm in relation to the WTMM algorithmand positive value the opposite. As shown, WTMM issues earlierdetections than Val. Values of the lags τ corresponding to the maximumand minimum values of the function are presented under the names argmaxand argmin respectively.

The present inventors also discovered that only 29.5% of seizuresrecognized as such by the WTMM method were also detected by thevalidated method, indicating that in its generic form and by design, thevalidated algorithm is less sensitive and more specific for seizuredetection than the WTMM algorithm.

The WTMM method, along with the other methods mentioned supra, surveydifferent but inter-dependent ECoG signal properties, thus expanding thebreadth and perhaps also the depth of insight into the spectral“structure” of epileptic seizures in a clinically relevant manner. TheWTMM method is well suited for estimations of changes in power variancewithin adjacent “short” time windows. The WTMM may be implemented inoff-line analysis applications given its relatively high algorithmiccomplexity. Of course, readily foreseeable advances in theminiaturization, speed, power, and energy efficiency of computationaldevices indicate the WTMM may be implemented in implantable devicesand/or real time applications.

Algorithmic and visual expert analysis consensus as to whatgrapho-elements define a ‘seizure’ seems to be highly dependent on whenduring the course of a ‘seizure’ a decision is made. In this context, itis noteworthy that AIF frequently reached a value of 1, indicative ofconcordance among all detection methods sometime after seizure onset andbefore its termination (as determined by any of the methods), providedsaid seizures reached a certain duration (20-30 s.) as discussed in moredetail in U.S. provisional patent application Ser. No. 61/547,567, filedon Oct. 14, 2011. In short, seizure onsets and terminations may be undercertain conditions universally undefinable by algorithmic or expertvisual analysis. A systematic investigation of the differences in signalspectral properties between the “preface”/“epilogue” and the “main body”of seizures was not performed. It is speculated that the presence of“start-up transients” (in a dynamical sense) and of temporo-spatialdispersion of the ictal signal (which impacts S/N) may be most prominentat the onset and termination of seizures. These and local and globalstate-dependencies of certain signal features, account in part for thetemporal fluctuations in algorithmic detection performance thatcharacterize these results.

Defining seizure energy as the product of the standard deviation of thepower of ECoG by its duration (in seconds), reveals that the WTMMalgorithm identifies as a continuum seizures that the validatedalgorithm detects as clusters of short seizures. The lack ofcorrespondence between a certain percentage of detections (2.9% for theWTMM method) and the validated algorithm may be partially attributed tobrief discontinuities in seizure activity as shown in FIG. 8. Thisphenomenon (“go-stop-go”) appears to be inherent to seizures (e.g., itis a general feature of intermittency associated with many dynamicalsystems). These discontinuities are also an “artifact” caused by thearchitecture of and parameters used in each algorithm. For example, thelonger the foreground window and the higher the order statistical filter(e.g., median vs. quartile), in the validated algorithm, the higher theprobability that “gaps” in seizure activity will occur. Clustering ofdetections is a strategy to manage dynamical or artifactual ictal“fragmentation,” and in this sense the WTMM algorithm avoids thesubjective biases and arbitrariness associated with human-imposedclustering rules.

The dependencies of seizure energy (defined as the product of thestandard deviation of the differentiated ECoG signal and seizureduration, in sec.) on seizure duration, for the set of icti detected bythe WTMM method is depicted in FIG. 10. A subset of seizures detected byall methods obeys a simple law of proportionality between energy(y-axis) and duration (x-axis, log scale, seconds), that is, the longerthe seizure, the larger its energy. However, this relationship is notinvariably linear for other detection algorithms, indicating thepresence of interesting scaling properties of seizure energy.

FIG. 11 shows the empirical “tail” of the conditional probabilitydistribution functions for: (a) Seizure durations (minimum duration: 2sec); (b) the logarithm of seizure energy as estimated with theValidated method (dashed) and the Wavelet Transform Maximum Modulus(solid).

The conditional probabilities of durations (FIG. 11 a) and of thelogarithm of energy of seizures (FIG. 11 b) provide additional supportthat their properties are partly a function of the method used for theirdetection. The validated and STA/LTA algorithms yield similar durationsbut different from those of the WTMM and r² methods, which are analogousto each other (FIG. 11 a). The distributions of the logarithm of seizureenergies as identified by each of the methods (FIG. 11 b) revealsadditional discrepancies as evidenced by the much narrower and shorter“tail” distribution of the validated algorithm compared to the others.

The medical and psycho-socio-economic burden imposed upon patients,caregivers and health systems by pharmaco-resistant epilepsies isenormous. Intracranial devices for automated detection, warning anddelivery of therapy, the presently preferred “line of attack” for anabundance of weighty reasons, would be insufficient to adequatelyaddress said burden at a global scale. Reliance on signals that whileextra-cerebral are under cortical modulation or control such as cardiacor motor and are altered by seizures, emerges as a viable researchdirection with potentially fruitful clinical applications.

The greater ease of implementation and lower cost of automated real-timedetection, warning and therapy systems based on extra-cerebral signals,compared to those requiring intracranial placement, makes them worthy ofinvestigation.

Cortical electrical activity has been the primary, if not sole, sourceof signals for visual or automated detection and quantification ofseizures in clinical use. The inextricable link between brain andepilepsy has historically impelled clinical neuroscientists to leaveunexploited the equally inextricable link between brain and body. Thebrain-epilepsy link has distracted us from certain severe limitations(for certain applications) inherent to the recording of cortical signalsfrom scalp or even directly from its surface, such as marked corticalsignal attenuation and filtering and limited access to neural sources(only about one-third of the neocortex is surveyable by scalp electrodesand subdural electrodes record little activity from the lateral andbottom walls of sulci). Yet, readily accessible sources that provideindirect but valuable information about the state of the brain,particularly during the ictal or postictal state, remain largelyuntapped.

The growing emphasis on widely accessible, cost-effective, good qualityhealth care in the context of expanding populations, especially inage-groups above 60 yr. in whom the incidence of epilepsy is high, andthe shrinking financial resources to support the requiredinfra-structure, pose an enormous challenge to patients whose seizuresare pharmaco-resistant as well as to epileptologists and functionalneurosurgeons. The properly placed emphasis on implantable intracranialdevices for automated seizure detection, warning and delivery of therapyin patients with drug-resistant seizures should be viewed in the contextthat even if economic resources were unlimited, human resources arestarkly small. Given the number of functional neurosurgeons in theUnited States (one source puts the number at 300, of which about 100work in epilepsy) is it realistic to pursue exclusively intracranialdevices to address the unmet needs of pharmaco-resistant patientsconservatively estimated (in the US) at 600,000? The deleteriousmedical, and psycho-social impact of intractable epilepsy and its highcost of care, along with the sophisticated human and technologicalresources needed to address them, qualifies this, in these authorsopinions, as a public health care problem. Indeed, scientific advancesregardless of their value may not translate into improved care ofepilepsy and lessen its burden, unless devices are broadly accessible;in short the challenge of ameliorating the global burden ofdrug-resistant epilepsies may exceed scientific and technological ones.If the answer to the question put forth a few lines above is in thenegative (intracranial devices will not meet the global burden) viablealternatives must be sought.

The utilization of certain extra-cerebral signals looms as one suchalternative. Cardiac (e.g., heart rate, EKG morphology) and motor(speed, direction and force of joint movements) signals are primecandidates for the following reasons: 1. Structures that form part ofthe central autonomic nervous system or are strongly interconnected withit, are common sites of epileptogenesis (e.g., amygdalae-hippocampi); 2.Spread of seizures out of the primary epileptogenic zone, is prevalentin pharmaco-resistant patients so that even if the site of origin is notpart of the central autonomic network, invasion of it by ictal activityis quite common; 3. Partial seizures particularly if complex, arecharacterized by either positive (e.g., motor automatisms, hypermotoricbehavior, clonic/myoclonic activity, focal increase in anti-gravitatorymuscle tone) or negative (e.g., motionless, focal loss ofantigravitatory muscle tone) phenomena that are stereotypical acrossseizures originating from the same site and appear relatively early inthe course of seizures; 4. Cardiac and motor signals are highly robust,easily recordable as they do not require implantable devices ordevelopment of ground breaking technology; EKG, actigraphs, 3-Daccelerometers are widely available commercially and are considerablyless costly than those required for use in the central nervous system(CNS); 5. Signals of cardiac and motor origin lack the high complexityor large dimensionality of those generated by the brain's cortex, aresimpler to process and analyze, and, thus, are less computationallyexpensive. Ease of computation allows the use of simpler, smallerdevices compared to those required for computation of cortical signalsand as they use less power, battery recharging or replacements are lessfrequent; 6. The neurosurgical procedures and potential associatedcomplications make implantable devices unappealing to a majority ofpharmaco-resistant patients that responded to a survey.

Among the numerous extra-cerebral signals usable for seizure detection,cardiac, have been the most extensively investigated. Tachycardia is acommon manifestation of partial seizures, occurring in almost 90% ofseizures of mesial temporal origin and precedes electrographic (asdetermined with scalp electrodes) and clinical onset in the majority ofthese seizures. (Tachycardia invariably occurs in primarily/secondarilygeneralized tonic-clonic seizures being higher in magnitude and longerduration than in partial seizures. Tachycardia with tonic-clonicseizures is multifactorial: neurogenic, metabolic, and exertional.) Froma cardiac rhythm perspective, the increases in heart rate temporallycorrelated with seizures are rarely pathologic, being of sinus origin;additionally their magnitude is unlikely to compromise cardiac output inhealthy individuals. Ictal tachycardia has a strong neurogenic componentreflective of either an increase in sympathetic or withdrawal ofparasympathetic activity; while increases in motor activity in referenceto the interictal state would augment its magnitude, tachycardia occursin subjects in whom seizures manifest with motionless. Bradycardia alsooccurs with seizures, albeit with much lower prevalence thantachycardia; so called “temporal lobe syncope”, denoting the loss ofconsciousness (without convulsive activity) during partial seizures iscaused by profound bradycardia.

In light of the potential to apply cardiac signals, and in particular ofexploiting changes in heart rate (increases or decreases relative to aninterictal baseline) for automated seizure detection, algorithms arebeing developed and tested to this end. In addition to detection andwarning of seizures, heart rate changes may be used to quantify: a)Relative duration defined as the time said changes spend above or belowan interictal reference value(s); b) Relative intensity corresponding tothe area under the curve or to the product of peak/bottom heart rate andduration (in sec.); c) Seizure frequency/unit time (e.g., month). Thechallenge of this detection modality for ambulatory clinicalapplications, is the ubiquitousness of heart rate changes with dailylife activities that may translate into large numbers of false positivedetections. Arousal from sleep, standing up from a recumbent position,climbing stairs, are but a few of the myriad daily life activitiesassociated with relative or absolute changes (e.g., increases) in heartrate. The discriminating power or positive predictive value (ictal vs.exertional) of this detection modality is currently the subject ofinvestigation in epilepsy monitoring units. Heart rate, among other(rate of change/slope; P-QRS-T morphology) markers, during seizures, arerecorded, analyzed and compared to those associated with protocolizedmotor activities (e.g., walking on a treadmill). Preliminary resultsshow that the magnitude of ictal increases in heart rate is sufficientlylarge compared to non-strenous exercises, so as to allow accuratedifferentiation and, consequently, detection of certain types of partialseizures. It would be naïve and incorrect to presume that univariate(e.g., heart rate changes alone) automated detection of seizures wouldyield worthwhile positive predictive power (PPV=number of true positivedetections/total number of detections) in ambulatory patients.Multivariate-based detection would be required to achieve satisfactoryperformance in a sufficiently large number of patients; ictal(reversible) changes in EKG morphology while less prevalent than inheart rate, have higher specificity and may increase considerably speedof detection (e.g., to within 3 heart beats). Visual analysis ofperi-ictal R-R plots, has led to the discovery of heart rate patternswith characteristic morphology that are reproducible among seizuressharing a common epileptogenic zone and appear to be a specific ictalmarker. One of these patterns resembles the letter “M” and indicatesheart rate changes during seizures may not be unidirectional ormonotonic: in this example, heart rate elevation is followed by a returntowards its interictal baseline, which in turn gives way to a secondelevation in rate. These fluctuations may be attributable, in part tothe co-existence of parasympathetic and cholinergic neurons within thesame autonomic nervous system structure; specifically, components of thecentral autonomic network such as the Dorsal Medial Hypothalamus, theParaventricular Nucleus of the hypothalamus and the Nucleus TractusSolitarius have dual cholinergic and noradrenergic innervation. Heartrate changes are also expected to be dependent on time of day(circadian), level of consciousness (awake vs. asleep), patient'sfitness level, activity level (walking vs. jogging), and emotional andcognitive states, as well as on ingestion of drugs with adrenergic orcholinergic actions.

Ictal motor activity (movement amplitude, direction, velocity and typeand number of muscles groups involved) recorded with actigraphs/3-Daccelerometers would enhance specificity of cardiac-based detection, asit is stereotypical across seizures originating from the samestructure(s). Use of ictal motor movements to detect seizuresindependent of heart rate or other sensors is actively underinvestigation. For example, a wrist accelerometer accurately detectedseven of eight tonic-clonic seizures, and nonseizure movements werereadily identified by patients thereby reducing the consequence of falsedetections. As wearable technologies advance, so do opportunities formore precise measurement of complicated seizure-related movements suchas automatisms.

Respiratory rate is markedly increased (also a neurogenic phenomenon)during seizures manifesting with tachycardia and its specificity may behigher than heart rate changes as its magnitude and pattern differ amplyfrom exertional increases in ventilation. Electrodermal (e.g. skinresistance, sudomotor) or vocal (e.g., non-formed vocalizations)activity, eyelid and ocular movements (gaze deviation, nystagmus),metabolic (e.g., profound normokalemic lactic acidosis with convulsions,hormonal (prolactin elevations with convulsion or certain partialseizures) or tissue stress (lactic acid, CK) indices may aid inextracerebral seizure detection.

Paradoxically, a potentially important hurdle in the path to adoption ofextra-cerebral detection of seizures is the markedly low sensitivity andother limitations of patient diaries, the universal “gold” metric or“ground truth” in epileptology. The rate of automated seizure detection,whether cerebrally or extra-cerebrally based, will be higher, possiblymuch higher in certain cases, than obtainable with diaries as not onlyclinical, but also “subclinical” seizures will be logged. This“limitation” or “inconvenience” that may discourage patients andepileptologists, is compounded by the absence of simultaneously recordedcortical activity, since direct proof cannot be furnished that a changein extra-cerebral indices, was indeed caused by a seizure. A simple, butpowerful means to overcome this hurdle is through the administration ofcomplex reaction time tests implementable in real-time, into hand-helddevices and triggered by changes in extra-cerebral signals such as EKG;in a cooperative, motivated patient, cardiac activity changes in thecontext of an abnormal response or failed test will be classified asclinical seizures, while those with a preserved response as eithersubclinical seizures or false positive detections.

Based on the existing evidence and body or work, it may be stated thatextra-cerebral automated detection, warning, logging of seizures anddelivery of therapy, looms as a useful, cost-effective and widelyaccessible option to better manage pharmaco-resistant epilepsies.

An embodiment of a medical device adaptable for use in implementing someaspects of embodiments of the present invention is provided in FIG. 1.As shown in FIG. 1, a system may involve a medical device system thatsenses body signals of the patient—such as brain or cardiac activity—andanalyzes those signals to identify one or more aspects of the signalthat may identify the occurrence of a seizure. The signal may beprocessed to extract (e.g., mathematically by an algorithm that computescertain values from the raw or partially processed signal) features thatmay be used to identify a seizure when compared to the inter-ictalstate. As shown in the right side of FIG. 1, the features may also begraphically displayed either in real time or subsequent to the event toenable visual confirmation of the seizure event and gain additionalinsight into the seizure (e.g., by identifying a seizure metricassociated with the seizure).

Turning now to FIG. 2, a block diagram depiction of a medical device 200is provided, in accordance with one illustrative embodiment of thepresent invention. In some embodiments, the medical device 200 may beimplantable, while in other embodiments the medical device 200 may becompletely external to the body of the patient.

The medical device 200 may comprise a controller 210 capable ofcontrolling various aspects of the operation of the medical device 200.The controller 210 is capable of receiving internal data or externaldata, and in one embodiment, is capable of causing a stimulation unit(not shown) to generate and deliver an electrical signal, a drug,cooling, or two or more thereof to one or more target tissues of thepatient's body for treating a medical condition. For example, thecontroller 210 may receive manual instructions from an operatorexternally, or may cause an electrical signal to be generated anddelivered based on internal calculations and programming. In otherembodiments, the medical device 200 does not comprise a stimulationunit. In either embodiment, the controller 210 is capable of affectingsubstantially all functions of the medical device 200.

The controller 210 may comprise various components, such as a processor215, a memory 217, etc. The processor 215 may comprise one or moremicrocontrollers, microprocessors, etc., capable of performing variousexecutions of software components. The memory 217 may comprise variousmemory portions where a number of types of data (e.g., internal data,external data instructions, software codes, status data, diagnosticdata, etc.) may be stored. The memory 217 may comprise one or more ofrandom access memory (RAM), dynamic random access memory (DRAM),electrically erasable programmable read-only memory (EEPROM), flashmemory, etc.

The medical device 200 may also comprise a power supply 230. The powersupply 230 may comprise a battery, voltage regulators, capacitors, etc.,to provide power for the operation of the medical device 200, includingdelivering the therapeutic electrical signal. The power supply 230comprises a power source that in some embodiments may be rechargeable.In other embodiments, a non-rechargeable power source may be used. Thepower supply 230 provides power for the operation of the medical device200, including electronic operations and the electrical signalgeneration and delivery functions. The power supply 230 may comprise alithium/thionyl chloride cell or a lithium/carbon monofluoride (LiCFx)cell if the medical device 200 is implantable, or may compriseconventional watch or 9V batteries for external (i.e., non-implantable)embodiments. Other battery types known in the art of medical devices mayalso be used.

The medical device 200 may also comprise a communication unit 260capable of facilitating communications between the medical device 200and various devices. In particular, the communication unit 260 iscapable of providing transmission and reception of electronic signals toand from a monitoring unit 270, such as a handheld computer or PDA thatcan communicate with the medical device 200 wirelessly or by cable. Thecommunication unit 260 may include hardware, software, firmware, or anycombination thereof.

The medical device 200 may also comprise one or more sensor(s) 212coupled via sensor lead(s) 211 to the medical device 200. The sensor(s)212 are capable of receiving signals related to a physiologicalparameter, such as the patient's heart beat, blood pressure, and/ortemperature, and delivering the signals to the medical device 200. Thesensor 212 may also be capable of detecting kinetic signal associatedwith a patient's movement. The sensor 212, in one embodiment, may be anaccelerometer. The sensor 212, in another embodiment, may be aninclinometer. In another embodiment, the sensor 212 may be an actigraph.In one embodiment, the sensor(s) 212 may be the same as implantedelectrode(s) 126, 128. In other embodiments, the sensor(s) 212 areexternal structures that may be placed on the patient's skin, such asover the patient's heart or elsewhere on the patient's torso. The sensor212, in one embodiment is a multimodal signal sensor capable ofdetecting various autonomic and neurologic signals, including kineticsignals associated with the patient's movement.

The seizure onset/termination module 280 is capable of detecting anepileptic event based upon one or more signals provided by body datacollection module 275. The seizure onset/termination module 280 canimplement one or more algorithms using the autonomic data and neurologicdata in any particular order, weighting, etc. The seizureonset/termination module 280 may comprise software module(s) that arecapable of performing various interface functions, filtering functions,etc. In another embodiment, the seizure onset/termination module 280 maycomprise hardware circuitry that is capable of performing thesefunctions. In yet another embodiment, the seizure onset/terminationmodule 280 may comprise hardware, firmware, software and/or anycombination thereof.

The seizure onset/termination unit 280 is capable of determining anepileptic event by application of a WTMM algorithm to one or moresignals provided by body data collection module 275 and/or seizureonset/termination module 280. The seizure onset/termination unit 280 maycomprise software module(s) that are capable of performing variousinterface functions, filtering functions, etc. In another embodiment,the seizure onset/termination unit 280 may comprise hardware circuitrythat is capable of performing these functions. In yet anotherembodiment, the seizure onset/termination unit 280 may comprisehardware, firmware, software and/or any combination thereof.

In addition to components of the medical device 200 described above, amedical device system may comprise a storage unit to store an indicationof at least one of seizure or an increased risk of a seizure. Thestorage unit may be the memory 217 of the medical device 200, anotherstorage unit of the medical device 200, or an external database, such asa local database unit 255 or a remote database unit 250. The medicaldevice 200 may communicate the indication via the communications unit260. Alternatively or in addition to an external database, the medicaldevice 200 may be adapted to communicate the indication to at least oneof a patient, a caregiver, or a healthcare provider.

In various embodiments, one or more of the units or modules describedabove may be located in a monitoring unit 270 or a remote device 292,with communications between that unit or module and a unit or modulelocated in the medical device 200 taking place via communication unit260. For example, in one embodiment, one or more of the body datacollection module 275 or the seizure onset/termination module 280 may beexternal to the medical device 200, e.g., in a monitoring unit 270.Locating one or more of the body data collection module 275 or theseizure onset/termination module 280 outside the medical device 200 maybe advantageous if the calculation(s) is/are computationally intensive,in order to reduce energy expenditure and heat generation in the medicaldevice 200 or to expedite calculation.

The monitoring unit 270 may be a device that is capable of transmittingand receiving data to and from the medical device 200. In oneembodiment, the monitoring unit 270 is a computer system capable ofexecuting a data-acquisition program. The monitoring unit 270 may becontrolled by a healthcare provider, such as a physician, at a basestation in, for example, a doctor's office. In alternative embodiments,the monitoring unit 270 may be controlled by a patient in a systemproviding less interactive communication with the medical device 200than another monitoring unit 270 controlled by a healthcare provider.Whether controlled by the patient or by a healthcare provider, themonitoring unit 270 may be a computer, preferably a handheld computer orPDA, but may alternatively comprise any other device that is capable ofelectronic communications and programming, e.g., hand-held computersystem, a PC computer system, a laptop computer system, a server, apersonal digital assistant (PDA), an Apple-based computer system, acellular telephone, etc. The monitoring unit 270 may download variousparameters and program software into the medical device 200 forprogramming the operation of the medical device, and may also receiveand upload various status conditions and other data from the medicaldevice 200. Communications between the monitoring unit 270 and thecommunication unit 260 in the medical device 200 may occur via awireless or other type of communication, represented generally by line277 in FIG. 2. This may occur using, e.g., wand 155 to communicate by RFenergy with an implantable signal generator 110. Alternatively, the wandmay be omitted in some systems, e.g., systems in which the MD 200 isnon-implantable, or implantable systems in which monitoring unit 270 andMD 200 operate in the MICS bandwidths.

In one embodiment, the monitoring unit 270 may comprise a local databaseunit 255. Optionally or alternatively, the monitoring unit 270 may alsobe coupled to a database unit 250, which may be separate from monitoringunit 270 (e.g., a centralized database wirelessly linked to a handheldmonitoring unit 270). The database unit 250 and/or the local databaseunit 255 are capable of storing various patient data. These data maycomprise patient parameter data acquired from a patient's body, therapyparameter data, seizure severity data, and/or therapeutic efficacy data.The database unit 250 and/or the local database unit 255 may comprisedata for a plurality of patients, and may be organized and stored in avariety of manners, such as in date format, severity of disease format,etc. The database unit 250 and/or the local database unit 255 may berelational databases in one embodiment. A physician may perform variouspatient management functions (e.g., programming parameters for aresponsive therapy and/or setting references for one or more detectionparameters) using the monitoring unit 270, which may include obtainingand/or analyzing data from the medical device 200 and/or data from thedatabase unit 250 and/or the local database unit 255. The database unit250 and/or the local database unit 255 may store various patient data.

One or more of the blocks illustrated in the block diagram of themedical device 200 in FIG. 2 may comprise hardware units, softwareunits, firmware units, or any combination thereof. Additionally, one ormore blocks illustrated in FIG. 2 may be combined with other blocks,which may represent circuit hardware units, software algorithms, etc.Additionally, any number of the circuitry or software units associatedwith the various blocks illustrated in FIG. 2 may be combined into aprogrammable device, such as a field programmable gate array, an ASICdevice, etc.

Turning now to FIG. 3, a block diagram depiction of an exemplaryimplementation of the body data collection module 275 is shown. The bodydata collection module 275 may include hardware (e.g., amplifiers,accelerometers), tools for chemical assays, optical measuring tools, abody data memory 350 (which may be independent of memory 117 or part ofit) for storing and/or buffering data. The body data memory 350 may beadapted to store body data for logging or reporting and/or for futurebody data processing and/or statistical analyses. Body data collectionmodule 275 may also include one or more body data interfaces 310 forinput/output (I/O) communications between the body data collectionmodule 275 and sensors 112. Body data from memory 350 and/or interface310 may be provided to one or more body index calculation unit(s) 355,which may determine one or more body indices.

In the embodiments of FIG. 3, sensors 112 may be provided as any ofvarious body data units/modules (e.g., autonomic data acquisition unit360, neurological data acquisition unit 370, endocrine data acquisitionunit 373, metabolic data acquisition unit 374, tissue stress marker dataacquisition unit 375, and physical fitness/integrity determination unit376) via connection 380. Connection 380 may be a wired connection awireless connection, or a combination of the two. Connection 380 may bea bus-like implementation or may include an individual connection (notshown) for all or some of the body data units.

In one embodiment, the autonomic data acquisition unit 360 may include acardiac data acquisition unit 361 adapted to acquire a phonocardiogram(PKG), EKG, echocardiography, apexcardiography and/or the like, a bloodpressure acquisition unit 363, a respiration acquisition unit 364, ablood gases acquisition unit 365, and/or the like. In one embodiment,the neurologic data acquisition unit 370 may contain a kinetic unit 366that may comprise an accelerometer unit 367, an inclinometer unit 368,and/or the like; the neurologic data acquisition unit 370 may alsocontain a responsiveness/awareness unit 369 that may be used todetermine a patient's responsiveness to testing/stimuli and/or apatient's awareness of their surroundings. Body data collection module275 may collect additional data not listed herein, that would becomeapparent to one of skill in the art having the benefit of thisdisclosure.

The body data units ([360-370], [373-377]) may be adapted to collect,acquire, receive/transmit heart beat data, EKG, PKG, echocardiogram,apexcardiogram, blood pressure, respirations, blood gases, bodyacceleration data, body inclination data, EEG/ECoG, quality of lifedata, physical fitness data, and/or the like.

The body data interface(s) 310 may include various amplifier(s) 320, oneor more A/D converters 330 and/or one or more buffers 340 or othermemory (not shown). In one embodiment, the amplifier(s) 320 may beadapted to boost and condition incoming and/or outgoing signal strengthsfor signals such as those to/from any of the body data acquisitionunits/modules (e.g., ([360-370], [373-377])) or signals to/from otherunits/modules of the MD 100. The A/D converter(s) 330 may be adapted toconvert analog input signals from the body data unit(s)/module(s) into adigital signal format for processing by controller 210 (and/or processor215). A converted signal may also be stored in a buffer(s) 340, a bodydata memory 350, or some other memory internal to the MD 100 (e.g.,memory 117, FIG. 1) or external to the MD 100 (e.g., monitoring unit170, local database unit 155, database unit 150, and remote device 192).The buffer(s) 340 may be adapted to buffer and/or store signals receivedor transmitted by the body data collection module 275.

As an illustrative example, in one embodiment, data related to apatient's respiration may be acquired by respiration unit 364 and sentto MD 100. The body data collection module 275 may receive therespiration data using body data interface(s) 310. As the data isreceived by the body data interface(s) 310, it may beamplified/conditioned by amplifier(s) 320 and then converted by A/Dconverter(s) into a digital form. The digital signal may be buffered bya buffer(s) 340 before the data signal is transmitted to othercomponents of the body data collection module 275 (e.g., body datamemory 350) or other components of the MD 100 (e.g., controller 110,processor 115, memory 117, communication unit 160, or the like). Bodydata in analog form may be also used in one or more embodiments.

Body data collection module 275 may use body data from memory 350 and/orinterface 310 to calculate one or more body indices in body one or morebody index calculation unit(s) 355. A wide variety of body indices maybe determined, including a variety of autonomic indices such as heartrate, blood pressure, respiration rate, blood oxygen saturation,neurological indices such as maximum acceleration, patient position(e.g., standing or sitting), and other indices derived from body dataacquisition units 360, 370, 373, 374, 375, 376, 377, etc.

Turning now to FIG. 4, an MD 100 (as described in FIG. 3) is provided,in accordance with one illustrative embodiment of the present invention.FIG. 4 depicts the body data acquisition units similar to those shown inFIG. 3, in accordance with another embodiment, wherein these unites areincluded within the MD 100, rather being externally coupled to the MD100, as shown in FIG. 3. In accordance with various embodiments, anynumber and type of body data acquisition units may be included withinthe MD 100, as shown in FIG. 4, while other body data units may beexternally coupled, as shown in FIG. 3. The body data acquisition unitsmay be coupled to the body data collection module 275 in a fashionsimilar to that described above with respect to FIG. 3, or in any numberof different manners used in coupling intra-medical device modules andunits. The manner by which the body data acquisition units may becoupled to the body data collection module 275 is not essential to, anddoes not limit, embodiments of the instant invention as would beunderstood by one of skill in the art having the benefit of thisdisclosure. Embodiments of the MD depicted in FIG. 4 may be fullyimplantable or may be adapted to be provided in a system that isexternal to the patient's body.

A time series body signal collected by the body data collection module275 may comprise at least one of a measurement of the patient's heartrate, a measurement of the patient's kinetic activity, a measurement ofthe patient's brain electrical activity, a measurement of the patient'soxygen consumption, a measurement of the patient's work, a measurementof an endocrine activity of the patient, a measurement of a metabolicactivity of the patient, a measurement of an autonomic activity of thepatient, a measurement of a cognitive activity of the patient, or ameasurement of a tissue stress marker of the patient.

Turning to FIG. 5, the seizure onset/termination unit 280 depicted inFIG. 2 is shown in greater detail. The seizure onset/termination unit280 may comprise a body signal interface 510 adapted to receivedcollected body data from the body data collection module 275. Forexample, the seizure onset/termination unit 280 may be adapted toreceive a time series of collected body data.

The seizure onset/termination unit 280 may also comprise a time windowcontrol module 520. The time window control module 520 may comprise aforeground window determination module 521, adapted to determine asliding time window for the time series body signal comprising aforeground window; and/or a background window determination module 522,adapted to determine a sliding time window for the time series bodysignal comprising a background window.

In other embodiments (not shown), any time window used herein may be amoving time window, not necessarily a sliding time window. As usedherein, a “sliding” time window moves over continuous points of a timeseries and the present (e.g., foreground) and past (e.g., background)are contiguous in time, if not overlapping. For example, if theforeground window is 5 s and the background 60 s in length, and thecurrent time is 10:00:00 AM, the temporal location of the foregroundwindow may be 10:00:00-10:00:05 and that of the background,09:59:00-10:00:00. A “moving” time window may move over continuouspoints of the time series or “jump” over discontinuous points. Forexample, a moving window may be chosen from past data that optimizessensitivity, specificity, or speed of detection as required by thepatient's prevailing conditions, activities, or time of day, said timebeing discontiguous from that of the foreground window. Using theexample cited immediately above, in this example, the foreground windowof 5 s at 10:00:00 AM may be compared to a 60 s background recorded 6hr. earlier (04:00:00-04:01:00). A moving window may also be the averageor median of several windows.

The seizure onset/termination unit 280 may also comprise a WTMM unit530. The WTMM unit 530 may be adapted to apply a WTMM skeleton to atleast one long chain extracted from the windowed signals using acontinuous wavelet transform.

The parameters of the WTMM may comprise at least one window length of 1second, a scale for selecting chain length of 3, and a varianceincrement of 0.25.

The parameters of the WTMM model may be selected based on at least oneof a clinical application of said detection; a level of safety riskassociated with an activity; at least one of an age, physical state, ormental state of the patient; a length of a window available for warning;a degree of efficacy of therapy and of its latency; a degree of seizurecontrol; a degree of circadian and ultradian fluctuations of saidpatient's seizure activity; a performance of the detection method as afunction of the patient's sleep/wake cycle or vigilance level; adependence of the patient's seizure activity on at least one of a levelof consciousness, a level of cognitive activity, or a level of physicalactivity; the site of seizure origin; a seizure type suffered by saidpatient; a desired sensitivity of detection of a seizure, a desiredspecificity of detection of a seizure, a desired speed of detection of aseizure, an input provided by the patient, or an input provided by asensor.

In various embodiments, the selected parameters may reflect the degreeof certainty of detections desired by the patient, a caregiver, amedical professional, or two or more thereof. Such person(s) areexpected to have biases regarding their desire for certainty ofdetection, and variation in their risk-proneness and/or aversion torisk. Thus, in one embodiment, the patient, caregiver, and/or medicalprofessional may be allowed to change (within certain limits and forcertain activities only, if desired) the sensitivity, specificity,and/or speed of detection of the algorithms.

The seizure onset/termination unit 280 may also comprise a variancedetermination unit 540. The variance determination unit 540 may beadapted to determine a variance of the output of the WTMM unit 530 basedupon the WTMM skeleton.

The seizure onset/termination unit 280 may also comprise a stepwiseapproximation unit 550. The stepwise approximation unit 550 may beadapted to generate a stepwise approximation of the changes in varianceof the windowed signal as analyzed by the WTMM unit 530 and/or thevariance determination unit 540.

The seizure onset/termination unit 280 may also comprise a seizure onsetdetermination module 560. The seizure onset determination module 560 maybe configured to determine an onset of a seizure in response to adetermination that an output of the stepwise approximation of thechanges in variance in the foreground window has reached an onsetthreshold.

The seizure onset/termination unit 280 may also comprise a seizuretermination determination module 570. The seizure terminationdetermination module 570 may be adapted to determine a termination of aseizure in response to a determination that an output of the stepwiseapproximation of the changes in variance in the background window hasreached a termination threshold.

Turning to FIG. 12, a flowchart depiction of a method for detecting anonset and a termination of an epileptic event from a patient body signalis shown. A time series body signal of a patient may be received at1210. Sliding first and second time windows for the time series bodysignal may be determined at 1220. The determination at 1220 may comprisedetermining a foreground window at 1225 and/or determining a backgroundwindow at 1227. A WTMM operation may be performed at 1230 to each of thesliding adjacent time windows. The performance at 1230 may compriseconstructing at 1232 a WTMM skeleton, determining at 1234 a variance ofthe output of the construction, and/or constructing at 1236 a stepwiseapproximation of change in the variance.

An onset of a seizure may be determined at 1240 based on the stepwiseapproximation and/or the variance. A termination of a seizure may bedetermined at 1250 based on the stepwise approximation and/or thevariance.

Optionally, the method depicted in FIG. 12 may comprise otheractivities. The method may further involve delivering a therapy for theseizure at a particular time at 1270, wherein at least one of thetherapy, the particular time, or both may be based upon thedetermination of the seizure onset.

Alternatively or in addition, the method may further involve determiningat 1280 an efficacy of the therapy.

Alternatively or in addition, the method may further involve issuing at1285 a warning for the seizure, wherein the warning may be based uponthe determination of the seizure onset.

In one embodiment, at least one of the delivered therapy or the issuedwarning is based at least in part on at least one of the type ofactivity engaged in by the patient at the time of seizure onset, theseizure type, the seizure severity, or the time elapsed from the lastseizure.

Alternatively or in addition, the method may further involve determiningat 1290 at least one of a timing of delivery of therapy, a type oftherapy, at least one parameter of the therapy, a timing of sending awarning, a type of warning, a duration of the warning, or an efficacy ofsaid therapy, based upon a timing of said determination of said seizureonset, said determination of said seizure termination, or both.

Alternatively or in addition, the method may further involve determiningat 1295 at least one value selected from the duration of the epilepticevent, the severity of the epileptic event, the intensity of theepileptic event, the extent of spread of the epileptic event, aninter-seizure interval between the epileptic event and a prior epilepticevent, a patient impact of the epileptic event, or a time of occurrenceof the epileptic event. The method may further comprise logging at 1296the at least one value.

A method, such as that depicted in FIG. 12, may be implemented by anon-transitory computer readable program storage unit encoded withinstructions that, when executed by a computer, perform the method.

An activity, such as walking, swimming, driving, etc., may be allowed orterminated, a warning may be issued or not issued, or a therapy may bedelivered or not delivered, based on the determination of seizure onset,seizure termination, or both, either from a WTMM algorithm alone or aPMSA value calculated at least in part from an indicator functionderived from the WTMM algorithm.

An “efficacy index” may be used herein to refer to any quantification ofan efficacious result of a therapy. In one example, if a patient'sseizures typically present an increase in heart rate from a resting rateof 80 beats per minute (BPM) to a peak ictal heart rate of 160 BPM, andupon administering a therapy to the patient, the patient's peak ictalheart rate is 110 BPM, this result may be quantified as an efficacyindex of 50 (on a scale of non-therapy peak ictal heart rate—peak ictalheart rate after therapy), 0.625 (50 BPM reduction from peak ictal heartrate/80 BPM increase from resting rate to peak ictal heart rate in theabsence of therapy), etc.

1. A non-transitory computer readable program storage unit encoded withinstructions that, when executed by a computer, perform a method,comprising: receiving a time series of a first body signal of thepatient, determining at least two sliding adjacent time windows for saidtime series of said first body signal comprising a foreground window anda background window; extracting at least one first long chain from saidforeground window and at least one second long chain from saidbackground window; applying a continuous wavelet transform to each ofsaid long chains, to yield a wavelet transform maximum modulus (WTMM)skeleton for each window; determining a first variance of the WTMMskeleton from said foreground window and a second variance of the WTMMskeleton from said background window; generating a stepwiseapproximation of changes in said variance; determining a seizure onsetin response to a determination that an output of the stepwiseapproximation of changes in said first variance has reached an onsetthreshold; and determining a seizure termination in response to adetermination that an output of the stepwise approximation of changes insaid second variance has reached a termination threshold.
 2. Thenon-transitory computer readable program storage unit of claim 1,including data that when executed by a processor performs the method ofclaim 1, wherein parameters of the wavelet transform maximum moduluscomprise at least one window length of 1 second, a scale for selectingchain length of 3, and a variance increment of 0.25.
 3. Thenon-transitory computer readable program storage unit of claim 1,including data that when executed by a processor performs the method ofclaim 1, wherein at least one of said onset threshold or saidtermination threshold is based on at least one of a clinical applicationof at least one said determination; a level of safety risk associatedwith an activity; at least one of an age, physical state, or mentalstate of the patient; a length of a window available for warning; adegree of efficacy of therapy and of its latency; a degree of seizurecontrol; a degree of circadian and ultradian fluctuations of saidpatient's seizure activity; a performance of the detection method as afunction of the patient's sleep/wake cycle or vigilance level; adependence of the patient's seizure activity on at least one of a levelof consciousness, a level of cognitive activity, or a level of physicalactivity; the site of seizure origin; a seizure type suffered by saidpatient; a desired sensitivity of detection of a seizure, a desiredspecificity of detection of a seizure, a desired speed of detection of aseizure, an input provided by the patient, or an input provided by asensor.
 4. The non-transitory computer readable program storage unit ofclaim 3, including data that when executed by a processor performs themethod of claim 3, wherein the method further comprises adaptivelyestablishing at least one of said onset threshold or said terminationthreshold.
 5. The non-transitory computer readable program storage unitof claim 1, including data that when executed by a processor performsthe method of claim 1, wherein said time series body signal comprises atleast one of a measurement of the patient's heart activity, ameasurement of the patient's respiratory activity, a measurement of thepatient's kinetic activity, a measurement of the patient's brainelectrical activity, a measurement of the patient's oxygen consumption,a measurement of the patient's oxygen saturation, a measurement of anendocrine activity of the patient, a measurement of a metabolic activityof the patient, a measurement of an autonomic activity of the patient, ameasurement of a cognitive activity of the patient, or a measurement ofa tissue stress marker of the patient.
 6. The non-transitory computerreadable program storage unit of claim 1, including data that whenexecuted by a processor performs the method of claim 1, wherein themethod further comprises at least one responsive action selected from:delivering a therapy for said seizure at a particular time, wherein atleast one of said therapy, said particular time, or both is based uponsaid determination of said seizure onset; determining an efficacy ofsaid therapy; or issuing a warning for said seizure, wherein saidwarning is based upon a determination of said seizure onset, adetermination of duration of said seizure, a determination of a type ofsaid seizure, or two or more thereof.
 7. The non-transitory computerreadable program storage unit of claim 6, including data that whenexecuted by a processor performs the method of claim 6, wherein at leastone of the delivered therapy or the issued warning is based at least inpart on at least one of the type of activity engaged in by the patientat the time of seizure onset, the seizure type, the seizure severity, orthe time elapsed from the last seizure.
 8. The non-transitory computerreadable program storage unit of claim 1, including data that whenexecuted by a processor performs the method of claim 1, wherein themethod further comprises determining at least one of: a timing ofdelivery of therapy, a duration of a therapy, a type of therapy, atleast one parameter of the therapy, a timing of sending a warning, atype of warning, or a duration of the warning; based upon said seizureonset, said seizure termination, or both.
 9. The non-transitory computerreadable program storage unit of claim 1, including data that whenexecuted by a processor performs the method of claim 1, wherein themethod further comprises: determining at least one value selected fromthe duration of said seizure, the severity of said seizure, theintensity of said seizure, the extent of spread of said seizure, aninter-seizure interval between said seizure and a prior seizure, apatient impact of said seizure, or a time of occurrence of said seizure;and logging said at least one value.
 10. The non-transitory computerreadable program storage unit of claim 1, including data that whenexecuted by a processor performs the method of claim 1, wherein themethod further comprises at least one of: determining an occurrence of aseizure based on the output of a WTMM algorithm on at least one secondbody signal, determining an occurrence of a seizure based on the outputof at least one second algorithm on said first body signal, ordetermining an occurrence of a seizure based on the output of at leastone second algorithm on said at least one second body signal.
 11. Thenon-transitory computer readable program storage unit of claim 10,including data that when executed by a processor performs the method ofclaim 10, wherein the second body signal is selected from an EKG signal,an accelerometer signal, or a signal indicative of a loss ofresponsiveness.
 12. The non-transitory computer readable program storageunit of claim 1, including data that when executed by a processorperforms the method of claim 1, wherein the method further comprisesestimating the degree of nonstationarity of said first body signal. 13.A method for detecting an onset and a termination of a seizure from apatient body signal, comprising: receiving a time series of a first bodysignal of the patient, determining at least two sliding adjacent timewindows for said time series of said first body signal comprising aforeground window and a background window; applying a wavelet transformmaximum modulus (WTMM) skeleton to at least one long chain extractedfrom said windowed signals using a continuous wavelet transform;determining a variance of the output of the previous step based upon theWTMM skeleton; generating a stepwise approximation of the changes invariance of the windowed signal as analyzed using previous steps;determining a seizure onset in response to a determination that anoutput of the stepwise approximation of the changes in variance in theforeground window has reached an onset threshold; and determining aseizure termination in response to a determination that an output of thestepwise approximation of the changes in variance in the backgroundwindow has reached a termination threshold.
 14. The method of claim 13,wherein the parameters of the wavelet transform maximum modulus compriseat least one window length of 1 second, a scale for selecting chainlength of 3, and a variance increment of 0.25.
 15. The method of claim13, wherein at least one of said onset threshold or said terminationthreshold is based on at least one of a clinical application of at leastone said determination; a level of safety risk associated with anactivity; at least one of an age, physical state, or mental state of thepatient; a length of a window available for warning; a degree ofefficacy of therapy and of its latency; a degree of seizure control; adegree of circadian and ultradian fluctuations of said patient's seizureactivity; a performance of the detection method as a function of thepatient's sleep/wake cycle or vigilance level; a dependence of thepatient's seizure activity on at least one of a level of consciousness,a level of cognitive activity, or a level of physical activity; the siteof seizure origin; a seizure type suffered by said patient; a desiredsensitivity of detection of a seizure, a desired specificity ofdetection of a seizure, a desired speed of detection of a seizure, aninput provided by the patient, or an input provided by a sensor.
 16. Themethod of claim 15, further comprising adaptively establishing at leastone of said onset threshold or said termination threshold.
 17. Themethod of claim 13, wherein said time series body signal comprises atleast one of a measurement of the patient's heart activity, ameasurement of the patient's respiratory activity, a measurement of thepatient's kinetic activity, a measurement of the patient's brainelectrical activity, a measurement of the patient's oxygen consumption,a measurement of the patient's oxygen saturation, a measurement of anendocrine activity of the patient, a measurement of a metabolic activityof the patient, a measurement of an autonomic activity of the patient, ameasurement of a cognitive activity of the patient, or a measurement ofa tissue stress marker of the patient.
 18. The method of claim 13,further comprising: delivering a therapy for said seizure at aparticular time, wherein at least one of said therapy, said particulartime, or both is based upon said determination of said seizure onset;determining an efficacy of said therapy; or issuing a warning for saidseizure, wherein said warning is based upon determination of saidseizure onset.
 19. The method of claim 18, wherein at least one of thedelivered therapy or the issued warning is based at least in part on atleast one of the type of activity engaged in by the patient at the timeof seizure onset, the seizure type, the seizure severity, or the timeelapsed from the last seizure.
 20. The method of claim 13, furthercomprising determining at least one of: a timing of delivery of therapy,a duration of a therapy, a type of therapy, at least one parameter ofthe therapy, a timing of sending a warning, a type of warning, or aduration of the warning; based upon said seizure onset, said seizuretermination, or both.
 21. The method of claim 13, further comprising:determining at least one value selected from the duration of saidseizure, the severity of said seizure, the intensity of said seizure,the extent of spread of said seizure, an inter-seizure interval betweensaid seizure and a prior seizure, a patient impact of said seizure, or atime of occurrence of said seizure; and logging said at least one value.22. The method of claim 13, further comprising at least one of:determining an occurrence of a seizure based on the output of a WTMMalgorithm on at least one second body signal, determining an occurrenceof a seizure based on the output of at least one second algorithm onsaid first body signal, or determining an occurrence of a seizure basedon the output of at least one second algorithm on said at least onesecond body signal.
 23. The method of claim 13, further comprisingestimating the degree of nonstationarity of said first body signal.